Merge pull request nschloe#311 from nschloe/pandas-mask

Pandas mask

Author: nschloe

Makefile

@@ -14,7 +14,7 @@ upload: setup.py
 	@if [ "$(shell git rev-parse --abbrev-ref HEAD)" != "master" ]; then exit 1; fi
 	rm -f dist/*
 	python3 setup.py sdist
-	python3 setup.py bdist_wheel --universal
+	python3 setup.py bdist_wheel
 	twine upload dist/*
 
 publish: tag upload

tikzplotlib/__about__.py

@@ -2,5 +2,5 @@
 __email__ = "[email protected]"
 __copyright__ = "Copyright (c) 2010-2019, {} <{}>".format(__author__, __email__)
 __license__ = "License :: OSI Approved :: MIT License"
-__version__ = "0.8.0"
+__version__ = "0.8.1"
 __status__ = "Development Status :: 5 - Production/Stable"

tikzplotlib/axes.py

@@ -636,10 +636,10 @@ def _handle_linear_segmented_color_map(cmap, data):
     # For an explanation of what _segmentdata contains, see
     # http://matplotlib.org/mpl_examples/pylab_examples/custom_cmap.py
     # A key sentence:
-    # If there are discontinuities, then it is a little more complicated.
-    # Label the 3 elements in each row in the cdict entry for a given color as
-    # (x, y0, y1).  Then for values of x between x[i] and x[i+1] the color
-    # value is interpolated between y1[i] and y0[i+1].
+    # If there are discontinuities, then it is a little more complicated.  Label the 3
+    # elements in each row in the cdict entry for a given color as (x, y0, y1).  Then
+    # for values of x between x[i] and x[i+1] the color value is interpolated between
+    # y1[i] and y0[i+1].
     segdata = cmap._segmentdata
     red = segdata["red"]
     green = segdata["green"]
@@ -695,18 +695,17 @@ def _handle_linear_segmented_color_map(cmap, data):
         if x >= 1.0:
             break
 
-    # The PGFPlots color map has an actual physical scale, like (0cm,10cm), and
-    # the points where the colors change is also given in those units. As of
-    # now (2010-05-06) it is crucial for PGFPlots that the difference between
-    # two successive points is an integer multiple of a given unity (parameter
-    # to the colormap; e.g., 1cm).  At the same time, TeX suffers from
-    # significant round-off errors, so make sure that this unit is not too
-    # small such that the round- off errors don't play much of a role. A unit
-    # of 1pt, e.g., does most often not work.
+    # The PGFPlots color map has an actual physical scale, like (0cm,10cm), and the
+    # points where the colors change is also given in those units. As of now
+    # (2010-05-06) it is crucial for PGFPlots that the difference between two successive
+    # points is an integer multiple of a given unity (parameter to the colormap; e.g.,
+    # 1cm).  At the same time, TeX suffers from significant round-off errors, so make
+    # sure that this unit is not too small such that the round- off errors don't play
+    # much of a role. A unit of 1pt, e.g., does most often not work.
     unit = "pt"
 
-    # Scale to integer (too high integers will firstly be slow and secondly may
-    # produce dimension errors or memory errors in latex)
+    # Scale to integer (too high integers will firstly be slow and secondly may produce
+    # dimension errors or memory errors in latex)
     # 0-1000 is the internal granularity of PGFplots.
     # 16300 was the maximum value for pgfplots<=1.13
     X = _scale_to_int(numpy.array(X), 1000)
@@ -776,15 +775,12 @@ def _handle_listed_color_map(cmap, data):
     return (colormap_string, is_custom_colormap)
 
 
-def _scale_to_int(X, max_val=None):
+def _scale_to_int(X, max_val):
+    """Scales the array X such that it contains only integers.
     """
-    Scales the array X such that it contains only integers.
-    """
-
-    if max_val is None:
-        X = X / _gcd_array(X)
-    else:
-        X = X / max(1 / max_val, _gcd_array(X))
+    # if max_val is None:
+    #     X = X / _gcd_array(X)
+    X = X / max(1 / max_val, _gcd_array(X))
     return [int(entry) for entry in X]
 
 

tikzplotlib/line2d.py

@@ -276,6 +276,10 @@ def _table(obj, data):
     else:
         if isinstance(ydata_mask, numpy.bool_) and not ydata_mask:
             ydata_mask = []
+        elif callable(ydata_mask):
+            # pandas.Series have the method mask
+            # https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.mask.html
+            ydata_mask = []
 
     axis_options = []